{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8379073a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['描述', '位置信息', '区域', '总价', '单价', '面积', '建成时间', '关注人数', '0室0厅', '0室1厅',\n",
       "       '1室0厅', '1室1厅', '1室2厅', '2室0厅', '2室1厅', '2室2厅', '3室0厅', '3室1厅', '3室2厅',\n",
       "       '3室3厅', '3室4厅', '4室0厅', '4室1厅', '4室2厅', '4室3厅', '4室4厅', '5室0厅', '5室1厅',\n",
       "       '5室2厅', '5室3厅', '5室4厅', '6室1厅', '6室2厅', '6室3厅', '6室4厅', '7室1厅', '7室2厅',\n",
       "       '7室3厅', '7室4厅', '7室5厅', '8室2厅', '8室3厅', '9室2厅', '双流', '大邑', '天府新区',\n",
       "       '天府新区南区', '崇州', '彭州', '成华', '新津', '新都', '武侯', '温江', '简阳', '蒲江', '郫都',\n",
       "       '都江堰', '金堂', '金牛', '锦江', '青白江', '青羊', '高新', '高新西', '龙泉驿', '毛坯', '简装',\n",
       "       '精装', '塔楼', '平房', '板塔结合', '板楼', '东', '南', '西', '北', '东北', '东南', '西南',\n",
       "       '西北', '中楼层', '低楼层', '高楼层', '总楼层'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler  # 标准化\n",
    "from sklearn.model_selection import train_test_split  # 划分测试集与训练集\n",
    "from sklearn.linear_model import LinearRegression as LR  # 回归模块\n",
    "\n",
    "# 在ipython中直接显示图像\n",
    "%matplotlib inline\n",
    "\n",
    "# 设置绘图显示中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']\n",
    "\n",
    "input_file_path = '../1.数据预处理/房产信息_预处理.xlsx'\n",
    "data = pd.read_excel(input_file_path)\n",
    "data.columns\n",
    "# print(data.columns)\n",
    "# print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "793897c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征提取\n",
    "total_price = data['总价']\n",
    "# print('总价：\\n', total_price)\n",
    "unit_price = data['单价']\n",
    "# print('单价：\\n', unit_price)\n",
    "house_area = data['面积']\n",
    "# house_area = data[['面积','单价']]\n",
    "# print('面积：\\n', house_area)\n",
    "house_type = data[['0室1厅','1室0厅', '1室1厅', '1室2厅', '2室0厅', '2室1厅', \\\n",
    "                   '2室2厅', '3室0厅', '3室1厅', '3室2厅','3室3厅', '3室4厅', \\\n",
    "                   '4室0厅', '4室1厅', '4室2厅', '4室3厅', '4室4厅', '5室0厅',\\\n",
    "                   '5室1厅','5室2厅', '5室3厅', '5室4厅', '6室1厅', '6室2厅', \\\n",
    "                   '6室3厅', '6室4厅', '7室1厅', '7室2厅','7室3厅', '7室4厅', \\\n",
    "                   '7室5厅', '8室2厅', '8室3厅', '9室2厅']]\n",
    "# print('户型：\\n', house_type)\n",
    "region = data[['双流', '大邑', '天府新区','天府新区南区', '崇州', '彭州', '成华', \\\n",
    "               '新津', '新都', '武侯', '温江', '简阳', '蒲江', '郫都','都江堰',   \\\n",
    "               '金堂', '金牛', '锦江', '青白江', '青羊', '高新']]\n",
    "# print('区域：\\n', region)\n",
    "house_class = data[['塔楼', '平房', '板塔结合', '板楼']]\n",
    "house_dirt = data[['东', '南', '西', '北', '东北', '东南', '西南','西北']]\n",
    "house_layer = data[['中楼层', '低楼层', '高楼层']]\n",
    "total_layer = data.总楼层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8a3c179d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('面积', np.float64(0.0012467942207168633)), ('0室1厅', np.float64(0.7689123958995713)), ('1室0厅', np.float64(0.3155312492322375)), ('1室1厅', np.float64(0.5154820842283591)), ('1室2厅', np.float64(0.6032287489049967)), ('2室0厅', np.float64(0.47286196302086897)), ('2室1厅', np.float64(0.579269445116352)), ('2室2厅', np.float64(0.6141259361632333)), ('3室0厅', np.float64(1.8222161051127006)), ('3室1厅', np.float64(0.6740071320210539)), ('3室2厅', np.float64(0.6850228281981992)), ('3室3厅', np.float64(0.6657193516490901)), ('3室4厅', np.float64(0.8582283791823443)), ('4室0厅', np.float64(0.26621568259683775)), ('4室1厅', np.float64(0.794076244298616)), ('4室2厅', np.float64(0.800188518432509)), ('4室3厅', np.float64(0.7520769275249956)), ('4室4厅', np.float64(1.1067018178368817)), ('5室0厅', np.float64(0.5288598807034016)), ('5室1厅', np.float64(0.9635440547220605)), ('5室2厅', np.float64(0.9942603486209495)), ('5室3厅', np.float64(1.1903352111644852)), ('5室4厅', np.float64(0.24227659918519462)), ('6室1厅', np.float64(0.8108953848800537)), ('6室2厅', np.float64(0.7786105425730745)), ('6室3厅', np.float64(1.4157701202281796)), ('6室4厅', np.float64(0.680088769033776)), ('7室1厅', np.float64(1.7826617007372354)), ('7室2厅', np.float64(1.2450659552365555)), ('7室3厅', np.float64(1.3943843474576607)), ('7室4厅', np.float64(-3.7692071686024065e-14)), ('7室5厅', np.float64(3.5867980659958367)), ('8室2厅', np.float64(1.18164773675328)), ('8室3厅', np.float64(5.6371574075342323e-14)), ('9室2厅', np.float64(1.5399294487416422)), ('双流', np.float64(0.034808897437787534)), ('大邑', np.float64(0.1856103240913201)), ('天府新区', np.float64(0.3067970483443283)), ('天府新区南区', np.float64(-0.0387897518902659)), ('崇州', np.float64(-0.17453488234064274)), ('彭州', np.float64(-0.6624340171493286)), ('成华', np.float64(0.4156638610381933)), ('新津', np.float64(-0.2421465391321232)), ('新都', np.float64(-0.20397095869160187)), ('武侯', np.float64(0.5404444030773328)), ('温江', np.float64(-0.22648696069036944)), ('简阳', np.float64(-0.2774462034607513)), ('蒲江', np.float64(0.8582283791823169)), ('郫都', np.float64(-0.1471638164467644)), ('都江堰', np.float64(-0.3304256196459055)), ('金堂', np.float64(-0.6545069602255781)), ('金牛', np.float64(0.2955508029956353)), ('锦江', np.float64(0.76867930719976)), ('青白江', np.float64(-0.5087499635500207)), ('青羊', np.float64(0.7044143346099168)), ('高新', np.float64(0.753073075493147)), ('塔楼', np.float64(0.03646768005350132)), ('平房', np.float64(-0.09052667398840997)), ('板塔结合', np.float64(0.06975437796172113)), ('板楼', np.float64(-0.015695384026738424)), ('东', np.float64(-0.010820525273874448)), ('南', np.float64(0.019894988957486648)), ('西', np.float64(0.03373429264892358)), ('北', np.float64(0.01437268372902939)), ('东北', np.float64(0.0039273801369987815)), ('东南', np.float64(-0.03705410574367063)), ('西南', np.float64(-0.0015668679793581775)), ('西北', np.float64(-0.0029830506663349743)), ('中楼层', np.float64(-0.0021654242946009816)), ('低楼层', np.float64(0.02807329218516208)), ('高楼层', np.float64(-0.025907867890507474)), ('总楼层', np.float64(0.016676171431900588))]\n",
      "0.2110689051974406\n"
     ]
    }
   ],
   "source": [
    "# 单变量回归\n",
    "X = pd.concat([house_area, house_type, region, house_class, house_dirt, \\\n",
    "               house_layer, total_layer], axis=1)\n",
    "# print('特征矩阵X：\\n', X)\n",
    "Y = unit_price\n",
    "# 设置训练集与测试集(二八原则，20%的数据作为测试集，80%的数据作为训练集)\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,Y,test_size=0.2, random_state=420)\n",
    "# print('训练集特征矩阵Xtrain：\\n', Xtrain)\n",
    "# print('测试集特征矩阵Xtest：\\n', Xtest)\n",
    "# print('训练集标签Ytrain：\\n', Ytrain)\n",
    "# print('测试集标签Ytest：\\n', Ytest)\n",
    "# 线性回归\n",
    "reg = LR().fit(Xtrain, Ytrain)\n",
    "# 预测\n",
    "yhat = reg.predict(Xtest)\n",
    "# 查看回归系数\n",
    "print(list(zip(X.columns, reg.coef_)))\n",
    "# 查看截距\n",
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "88d1507f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x219b0818190>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制前n条记录\n",
    "n = 50\n",
    "# 绘制模型估计值\n",
    "plt.plot(range(len(yhat[:n])), yhat[:n])\n",
    "# print('模型估计值：\\n', len(yhat[:n]),range(len(yhat[:n])))\n",
    "# print('模型估计值：\\n', yhat[:n])\n",
    "# 绘制模型实际值\n",
    "# print('模型实际值：\\n', len(Ytest[:n]), range(len(Ytest[:n])))\n",
    "# print('模型实际值：\\n', Ytest[:n])\n",
    "plt.plot(range(len(Ytest[:n])), Ytest[:n])\n",
    "\n",
    "# 图形设置\n",
    "plt.xlabel('个例')\n",
    "plt.ylabel('单价')\n",
    "plt.title('线性回归预测结果')\n",
    "plt.legend([\"预估\",\"实际\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e476943c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE : 0.17848099922198063\n",
      "MAE : 0.302555441686002\n",
      "R2 : 0.5421389807993121\n",
      "调整R2 ：0.5370547745404519\n"
     ]
    }
   ],
   "source": [
    "# 用于检验模型效果\n",
    "from sklearn.metrics import mean_squared_error  # MSE\n",
    "from sklearn.metrics import mean_absolute_error  # MAE\n",
    "from sklearn.metrics import r2_score  # R2\n",
    "\n",
    "mse = mean_squared_error(Ytest, yhat)  # MSE\n",
    "mae = mean_absolute_error(Ytest, yhat)  # MAE\n",
    "r2 = r2_score(Ytest, yhat)  # R2\n",
    "\n",
    "# 调整R2\n",
    "n = Xtest.shape[0]\n",
    "k = Xtest.shape[1]\n",
    "adj_r2 = 1-(1-r2)*((n-1)/(n-k-1))\n",
    "\n",
    "print('MSE : ' + str(mse))\n",
    "print('MAE : ' + str(mae))\n",
    "print('R2 : ' + str(r2))\n",
    "print('调整R2 ：' + str(adj_r2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
